引入极值非相邻连接的连接聚类方法

doi:10.13229/j.cnki.jdxbgxb201605034

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (5): 1616-1621.doi: 10.13229/j.cnki.jdxbgxb201605034

引入极值非相邻连接的连接聚类方法

王贵参^1,2, 黄岚^1,2, 王岩^1,2, 宋立明^1,2, 欧歌^1,2

1.吉林大学计算机科学与技术学院,长春130012;
2.吉林大学符号计算与知识工程教育部重点实验室,长春 130012

收稿日期:2015-06-20 出版日期:2016-09-20 发布日期:2016-09-20
通讯作者: 黄岚(1974-), 女, 教授, 博士生导师.研究方向:社会网络与数据挖掘,计算智能.E-mail:huanglan@jlu.edu.cn
作者简介:王贵参(1987-), 男, 博士研究生.研究方向:社会网络与数据挖掘.E-mail:clouddragon2@qq.com
基金资助:
国家自然科学基金项目(61472159, 61572227).

Link clustering method based on maxima and minima non-neighbor link similarity

WANG Gui-shen^1,2, HUANG Lan^1,2, WANG Yan^1,2, SONG Li-ming^1,2, OU Ge^1,2

1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
2.Key Laboratory of Symbol Computation and Knowledge Engineering,Ministry of Education, Changchun 130012, China

Received:2015-06-20 Online:2016-09-20 Published:2016-09-20

摘要/Abstract

摘要： 针对在连接相似度的计算过程中原始连接聚类(LC)方法并未考虑非相邻连接的相似关系,本文提出的极值非相邻连接相似度策略,弥补了原有连接相似度的不足。新的极值非相邻连接相似度(MLS)策略考虑了连接之间相似关系的邻居节点集合的最大、最小情况。在此基础上,结合EQ评估策略,给出了新的引入极值非相邻连接的连接聚类(MLC)方法。在3组测试数据集上的实验结果表明:本文MLC方法相比原始LC、经典重叠社区发现(CPM)方法和扩展的连接聚类ELC方法在多种评估指标上表现优异。

关键词: 人工智能, 连接聚类, 连接相似度, 重叠社区发现, 相邻连接, 非相邻连接

Abstract: Traditional clustering methods can be applied to the research of overlapping community detection directly with link similarity. However, Link Clustering (LC) method does not consider the relationship between non-neighbor links in the calculation of link similarity. In this paper, two link similarity strategies are proposed based on the maxima and minima non-neighbor (MLS) in order to overcome the shortcomings of the original link clustering. The two link similarity strategies consider the minimum and maximum conditions of link similarity relationships. Then, a link clustering method is put forward based on MLS strategies (MLC) with EQ evaluation. Experimental results on three real-world networks show that the proposed MLC method achieves better performance than the original LC method, classical CPM method and Extended Link Clustering (ELC) method under several evaluation criteria.

Key words: artificial intelligence, link clustering, link similarity, overlapping community detection, neighbor links, non-neighbor links

中图分类号:

TP301

王贵参, 黄岚, 王岩, 宋立明, 欧歌. 引入极值非相邻连接的连接聚类方法[J]. 吉林大学学报(工学版), 2016, 46(5): 1616-1621.

WANG Gui-shen, HUANG Lan, WANG Yan, SONG Li-ming, OU Ge. Link clustering method based on maxima and minima non-neighbor link similarity[J]. 吉林大学学报(工学版), 2016, 46(5): 1616-1621.

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引入极值非相邻连接的连接聚类方法

Link clustering method based on maxima and minima non-neighbor link similarity

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